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FONDAZIONE CMCC

FONDAZIONE CENTRO EURO-MEDITERRANEOSUI CAMBIAMENTI CLIMATICI
Country: Italy
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143 Projects, page 1 of 29
  • Funder: EEA Project Code: ETC CCA

    European Topic Centres (ETCs) are centres of thematic expertise contracted by the EEA to carry out specific tasks identified in the EEA Multiannual Work Programme and the annual work programmes. They are designated by the EEA Management Board following a Europe-wide competitive selection process and work as extensions of the EEA in specific topic areas. Each ETC consists of a lead organisation and specialist partner organisations from the environmental research and information community, which combine their resources in their particular areas of expertise. The ETC/CCA activities on climate change, impacts, vulnerability, adaptation and disaster risk reduction include: -harmonisation, quality assessment and exchange of data and/or information; -processing of climate-related information, including use of models, mapping, analyses, evaluations and (thematic, sectoral and integrated) assessments to describe and analyse the status of the environment in support to sound policy decision making; -update, improvement and development of indicators to communicate the findings to various users; -content maintenance, improvement and further development of Climate-ADAPT; -provision of capacity building in EEA countries.

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  • Funder: EC Project Code: 101065820
    Funder Contribution: 280,203 EUR

    Seasonal forecasting is a field with enormous potential influence in different socio-economic sectors, such as water resources, agriculture, health, and energy. Yet, surface climate conditions in Europe still represent a hurdle to formulate skillful seasonal predictions. SD4SP aims to improve simulation and prediction of the remote influence of two dominant tropical variability modes in the North Atlantic-European (NAE) region: El Niño-Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO); which are the leading modes of interannual variability in the tropical troposphere and stratosphere respectively. ENSO is highly predictable and constitutes the cornerstone of seasonal forecasting. The QBO is well constrained by initialization and has a long persistence, being considered as the most promising source of seasonal forecast quality with ENSO. However, many scientific questions remain unresolved concerning their tropical-extratropical teleconnections, and model systematic errors only worsen the problem. SD4SP will focus on the stratospheric pathway of the ENSO/QBO teleconnections to NAE and pursue gaining insight into the dynamical mechanisms at play. This goal will be undertaken by carrying out an unprecedented set of idealized seasonal forecast experiments to address the contribution of the tropical stratosphere and the polar stratosphere to the prediction skill by suppressing variability in the two stratospheric regions, separately. SD4SP is very timely in helping to reduce model biases and to increase current seasonal forecasting capabilities for the NAE surface climate. The goal of SD4SP is twofold: to identify key sources of predictability and to improve understanding and simulation of the mechanisms responsible for that predictability. SD4SP will bring together theory and applicability disentangling atmospheric teleconnections to satisfactorily exploit them in a seasonal prediction context.

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  • Funder: EC Project Code: 101028505
    Overall Budget: 183,473 EURFunder Contribution: 183,473 EUR

    The Paris Agreement is the first international accord to engage with the full-scale investment efforts required to address climate change. Moreover, the Sustainable Development Goals (SDGs) have emerged as the global benchmark for directing investments to the clean energy sector. And yet, neither scholarship nor practice has illuminated the combined implications of the Paris Agreement and SDGs for determining how businesses can evolve to maintain their competitiveness and fuel the energy transitions to a net-zero emissions society by the second half of the century (Article 4(1) of the Paris Agreement). In this context, the European Union (EU) starts from a vantage point, which is an ambitious internal action plan toward decarbonization (e.g. the European Green Deal (EGD)), but policy design fails to combine Europe's external and internal obligations to support decarbonization technology. This project focuses on one of the most important areas for decarbonization policies, Energy Storage Technology (EST) for renewable energy, to propose a refined regulatory framework to fulfill the EU's external and internal commitments in climate policy. The project investigates how sustainable investment in renewable energy storage can be fostered through effective policy design that includes prosumers (energy-producing consumers), starting in the EU. Empowering and coordinating prosumers and businesses through effective policy design is one of the most promising strategies for the EU to achieve its vision of a citizen-oriented energy future and 100% renewable energy system by 2050. The multidisciplinary nature of the project is strong, involving policy design, EU and international law and sustainable finance. It includes a two-way transfer of knowledge and the training of the candidate in advanced techniques in energy and climate economics. In line with the EGD, the proposed policy is poised to increase EU competitiveness in EST for renewable energy, and results are transferrable.

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  • Funder: EC Project Code: 707683
    Overall Budget: 180,277 EURFunder Contribution: 180,277 EUR

    Unlike extreme disasters, smaller scale disaster events receive relatively little attention in Climate Change and Disaster studies even though they occur more frequently and cause considerable damage and disruption to local economic, social, and environmental systems. This project looks at the impact and response generated by extensive disaster events in three regions in Italy as a means of furthering understanding of vulnerability and risk to recurring natural hazards. The project holds significant policy relevance in the fields of development, disaster risk reduction, and climate change adaptation. Despite their cumulative impact, small disasters are frequently left out of national disaster databases, and do not form the focus of national climate change or disaster management policies. As demonstrated by Marulanda et al (2010), the accumulated economic, social and environmental cost of small scale disasters can be higher in comparison to high impact, low frequency events occurring over the same time period. Small disasters are also important because they reveal underlying local development and planning issues that form the root cause of vulnerability to more extreme events. The objectives of this project include 1) a conceptual assessment of mechanisms for capturing data on disaster losses to analyze how definitions impact data accuracy for measuring extensive risk; 2) using alternative sources to build on existing datasets in order to assess the economic, social, and environmental losses associated with extensive disasters for three regions in Italy; 3) examining how disaster management institutions and communities respond to small scale and recurrent disasters, and if such events trigger changes in risk perception, disaster management, and learning at both institutional and community levels; 4) comparisons between quantitative and qualitative impacts of disaster events, and institutional regimes, hazard contexts, and cultural norms for confronting risk.

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  • Funder: EC Project Code: 101065985
    Funder Contribution: 172,750 EUR

    Cyclones form frequently over the Mediterranean Sea. The most intense systems cause extensive damage in the region and beyond. The ability to make climate predictions several months in advance of such extreme events has a large number of crucial socio-economic applications for disaster risk reduction. State-of the-art Seasonal Prediction Systems exhibit a good skill in predicting anomalies in the seasonal mean of meteorological fields such as temperature and precipitation. The models’ ability to reproduce variations in the occurrence of extreme events tends to be however much lower. This is particularly true in regions such as the Mediterranean, where extreme events are often driven by small scale processes that are not well reproduced at the resolution at which SPS typically run. The use of artificial intelligence techniques such as machine learning in the study of climate has gained great traction in recent years. The power of those techniques lies in the ability to detect patterns in large datasets without having to make explicit statistical assumptions, allowing to build predictive model whose skill continuously improves as the volume of data on which the models are trained increases. One promising field of application is to use artificial intelligence to improve the prediction of extremes in climate models. In this setting a machine learning model is trained to find relationships between large-scale climate variables (for which dynamical models have a good predictive skill) and the occurrence of extremes. The aim of this project is to improve the predictability of intense Mediterranean cyclones combining advanced artificial intelligence techniques and a state-of-the-art dynamical seasonal prediction system. The project will not only contribute to the knowledge of climate extremes predictability, but also lead to the implementation of a pre-operational dynamical-statistical prediction system with the potential to be extended to include different extremes.

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